Learning From Social Interactions: Personalized Pricing and Buyer Manipulation

2026-03-27

AI Summary

This study addresses how online platforms leverage buyers’ social connections to infer preferences for personalized pricing, while buyers may strategically manipulate their social interactions to avoid unfavorable prices. The paper proposes the first bi-level information asymmetry game-theoretic model that integrates social homophily, Bayesian learning, and mechanism design to capture buyers’ strategic behavior when anticipating the platform’s use of their social data. The analysis reveals that high-preference buyers’ attempts at manipulation can backfire, yet sellers still derive substantial benefits from social learning, with buyers’ strategic actions exerting only limited impact on platform revenue. These findings suggest that platforms should transparently utilize social data within regulatory compliance to enhance pricing robustness and foster user trust.

Abstract

As the sociological theory of homophily suggests, people tend to interact with those of similar preferences. Motivated by this well-established phenomenon, today's online sellers, such as Amazon,~seek~to learn a new buyer's private preference from his friends' purchase records. Although such learning allows the seller to enable personalized pricing and boost revenue, buyers are also increasingly aware of these practices and may alter their social behaviors accordingly. This paper presents the first study regarding how buyers strategically manipulate their social interaction signals considering their preference correlations, and how a seller can take buyers' strategic social behaviors into consideration when designing the pricing scheme. Starting with the fundamental two-buyer network, we propose and analyze a parsimonious model that uniquely captures the double-layered information asymmetry between the seller and buyers, integrating both individual buyer information and inter-buyer correlation information. Our analysis reveals that only high-preference buyers tend to manipulate their social interactions to evade the seller's personalized pricing, but surprisingly, their payoffs may actually worsen as a result. Moreover, we demonstrate that the seller can considerably benefit from the learning practice, regardless of whether the buyers are aware of this fact or not. Indeed, our analysis reveals that buyers' learning-aware strategic manipulation has only a slight impact on the seller's revenue. In light of the tightening regulatory policies concerning data access, it is advisable for sellers to maintain transparency with buyers regarding their access to buyers' social interaction data for learning purposes. This finding aligns well with current informed-consent industry practices for data sharing.

Problem

• personalized pricing
• social interactions
• buyer manipulation
• preference correlation
• information asymmetry

Innovation

• personalized pricing
• social manipulation
• information asymmetry
• strategic behavior
• preference correlation